{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigating Noise\n",
    "\n",
    "In this example, we investigate how a program might behave on a near-term device that is subject to noise using the convenience function `add_decoherence_noise`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyquil.quil import Program\n",
    "from pyquil.paulis import PauliSum, PauliTerm, exponentiate, exponential_map, trotterize\n",
    "from pyquil.gates import MEASURE, H, Z, RX, RZ, CZ\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The task\n",
    "We want to prepare $e^{i \\theta XY}$ and measure it in the $Z$ basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import pi\n",
    "theta = pi/3\n",
    "xy = PauliTerm('X', 0) * PauliTerm('Y', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The idiomatic Pyquil program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H 0\n",
      "RX(pi/2) 1\n",
      "CNOT 0 1\n",
      "RZ(2*pi/3) 1\n",
      "CNOT 0 1\n",
      "H 0\n",
      "RX(-pi/2) 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prog = exponential_map(xy)(theta)\n",
    "print(prog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The compiled program\n",
    "To run on a real device, we must compile each program to the native gate set for the device. The high-level noise model is similarly constrained to use a small, native gate set. In particular, we can use\n",
    "\n",
    " - $I$\n",
    " - $RZ(\\theta)$\n",
    " - $RX(\\pm \\pi/2)$\n",
    " - $CZ$\n",
    "\n",
    "For simplicity, the compiled program is given below but generally you will want to use a compiler to do this step for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_compiled_prog(theta):\n",
    "    return Program([\n",
    "        RZ(-pi/2, 0),\n",
    "        RX(-pi/2, 0),\n",
    "        RZ(-pi/2, 1),\n",
    "        RX( pi/2, 1),\n",
    "        CZ(1, 0),\n",
    "        RZ(-pi/2, 1),\n",
    "        RX(-pi/2, 1),\n",
    "        RZ(theta, 1),\n",
    "        RX( pi/2, 1),\n",
    "        CZ(1, 0),\n",
    "        RX( pi/2, 0),\n",
    "        RZ( pi/2, 0),\n",
    "        RZ(-pi/2, 1),\n",
    "        RX( pi/2, 1),\n",
    "        RZ(-pi/2, 1),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scan over noise parameters\n",
    "We perform a scan over three levels of noise each at 20 theta points.\n",
    "\n",
    "Specifically, we investigate T1 values of 1, 3, and 10 us. By default, T2 = T1 / 2, 1 qubit gates take 50 ns, and 2 qubit gates take 150 ns. \n",
    "\n",
    "In alignment with the device, $I$ and parametric $RZ$ are noiseless while $RX$ and $CZ$ gates experience 1q and 2q gate noise, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyquil.api import QVMConnection\n",
    "cxn = QVMConnection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.        ,   3.16227766,  10.        ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1s = np.logspace(-6, -5, num=3)\n",
    "thetas = np.linspace(-pi, pi, num=20)\n",
    "t1s * 1e6 # us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyquil.noise import add_decoherence_noise\n",
    "records = []\n",
    "for theta in thetas:\n",
    "    for t1 in t1s:\n",
    "        prog = get_compiled_prog(theta)\n",
    "        noisy = add_decoherence_noise(prog, T1=t1).inst([\n",
    "            MEASURE(0, 0),\n",
    "            MEASURE(1, 1),\n",
    "        ])\n",
    "        bitstrings = np.array(cxn.run(noisy, [0,1], 1000))\n",
    "        \n",
    "        # Expectation of Z0 and Z1\n",
    "        z0, z1 = 1 - 2*np.mean(bitstrings, axis=0)\n",
    "        \n",
    "        # Expectation of ZZ by computing the parity of each pair\n",
    "        zz = 1 - (np.sum(bitstrings, axis=1) % 2).mean() * 2 \n",
    "        \n",
    "        record = {\n",
    "            'z0': z0,\n",
    "            'z1': z1,\n",
    "            'zz': zz,\n",
    "            'theta': theta,\n",
    "            't1': t1,\n",
    "        }\n",
    "        records += [record]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='ticks', palette='colorblind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108172210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_all = pd.DataFrame(records)\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12,4))\n",
    "\n",
    "for t1 in t1s:\n",
    "    df = df_all.query('t1 == @t1')\n",
    "    \n",
    "    ax1.plot(df['theta'], df['z0'], 'o-')    \n",
    "    ax2.plot(df['theta'], df['z1'], 'o-')    \n",
    "    ax3.plot(df['theta'], df['zz'], 'o-', label='T1 = {:.0f} us'.format(t1*1e6))\n",
    "    \n",
    "ax3.legend(loc='best')\n",
    "\n",
    "ax1.set_ylabel('Z0')\n",
    "ax2.set_ylabel('Z1')\n",
    "ax3.set_ylabel('ZZ')\n",
    "ax2.set_xlabel(r'$\\theta$')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
